MAINTAG - Multi-Agent-based Predictive Maintenance Dataset Tagging System

Abstract

With the ongoing digitization of global activities, the number of predictive maintenance datasets has been steadily growing. These datasets are often manually classified – in literature review papers – to assess their relevance for predictive maintenance applications. However, this manual approach is increasingly unsustainable, as it is time-intensive, prone to errors, and the accelerating pace at which new datasets emerge in both scientific and industrial contexts. To overcome these challenges, there is a growing need for automated solutions to curate, analyze, and categorize (tag) datasets in the literature. To this end, we propose and evaluate MAINTAG (Multi-Agent-based Predictive Maintenance Dataset Tagging System), a novel multi-agent system designed to automate the classification of predictive maintenance datasets. MAINTAG is compatible with any criteria-based taxonomy and is assessed by benchmarking its tagging accuracy against recent state-of-the-art literature. This evaluation highlights MAINTAG’s ability to replicate expert-level dataset tagging.

MAINTAG resolves three critical challenges in predictive maintenance dataset tagging. The first one arises from the dramatic increase in the number of datasets. The recent growth in predictive maintenance applications has resulted in higher data volume. This makes manual tagging methods impractical and unscalable. Researchers, therefore, will struggle to manually process this flood of new data. The second challenge is related to the complexity of data classification. Each dataset consist of different data types, sources, and structures. These create additional classification difficulties. The third challenge is keeping data tagging consistent across different domains. Predictive maintenance datasets come from multiple industries, and each has its own set of standards and/or classification criteria. MAINTAG proposes a specialized multi-agent framework in response to these challenges. The framework divides the complex classification tasks into simpler and more manageable parts. The system uses specialized agents working in parallel to handle different classification tasks. These agents work together to analyze different aspects of the datasets. This system marks a major leap in autonomous data tagging. It also sets the foundation for a more scalable approach for handling surge in the predictive maintenance datasets.